Overview

Dataset statistics

Number of variables19
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.4 MiB
Average record size in memory152.0 B

Variable types

NUM17
CAT1
BOOL1

Warnings

GT Compressor inlet air temperature (T1) [C] has constant value "10000" Constant
GT Compressor inlet air pressure (P1) [bar] has constant value "10000" Constant
Ship speed (v) [knots] is highly correlated with Lever position (lp) [ ] and 12 other fieldsHigh correlation
Lever position (lp) [ ] is highly correlated with Ship speed (v) [knots] and 12 other fieldsHigh correlation
Gas Turbine shaft torque (GTT) [kN m] is highly correlated with Lever position (lp) [ ] and 12 other fieldsHigh correlation
Gas Turbine rate of revolutions (GTn) [rpm] is highly correlated with Lever position (lp) [ ] and 12 other fieldsHigh correlation
Gas Generator rate of revolutions (GGn) [rpm] is highly correlated with Lever position (lp) [ ] and 10 other fieldsHigh correlation
Starboard Propeller Torque (Ts) [kN] is highly correlated with Lever position (lp) [ ] and 12 other fieldsHigh correlation
Port Propeller Torque (Tp) [kN] is highly correlated with Lever position (lp) [ ] and 12 other fieldsHigh correlation
HP Turbine exit temperature (T48) [C] is highly correlated with Lever position (lp) [ ] and 12 other fieldsHigh correlation
GT Compressor outlet air temperature (T2) [C] is highly correlated with Lever position (lp) [ ] and 12 other fieldsHigh correlation
HP Turbine exit pressure (P48) [bar] is highly correlated with Lever position (lp) [ ] and 12 other fieldsHigh correlation
GT Compressor outlet air pressure (P2) [bar] is highly correlated with Lever position (lp) [ ] and 12 other fieldsHigh correlation
Gas Turbine exhaust gas pressure (Pexh) [bar] is highly correlated with Lever position (lp) [ ] and 12 other fieldsHigh correlation
Turbine Injecton Control (TIC) [%] is highly correlated with Lever position (lp) [ ] and 11 other fieldsHigh correlation
Fuel flow (mf) [kg/s] is highly correlated with Lever position (lp) [ ] and 11 other fieldsHigh correlation
Unnamed: 0 has unique values Unique
Turbine Injecton Control (TIC) [%] has 626 (6.3%) zeros Zeros

Reproduction

Analysis started2020-11-12 06:15:42.226776
Analysis finished2020-11-12 06:17:56.165382
Duration2 minutes and 13.94 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

Unnamed: 0
Real number (ℝ≥0)

UNIQUE

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4999.5
Minimum0
Maximum9999
Zeros1
Zeros (%)< 0.1%
Memory size78.1 KiB
2020-11-12T11:47:56.354007image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile499.95
Q12499.75
median4999.5
Q37499.25
95-th percentile9499.05
Maximum9999
Range9999
Interquartile range (IQR)4999.5

Descriptive statistics

Standard deviation2886.89568
Coefficient of variation (CV)0.5774368797
Kurtosis-1.2
Mean4999.5
Median Absolute Deviation (MAD)2500
Skewness0
Sum49995000
Variance8334166.667
MonotocityStrictly increasing
2020-11-12T11:47:56.666487image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
20471< 0.1%
 
95181< 0.1%
 
74811< 0.1%
 
54321< 0.1%
 
95261< 0.1%
 
33791< 0.1%
 
13301< 0.1%
 
74731< 0.1%
 
54241< 0.1%
 
33711< 0.1%
 
Other values (9990)999099.9%
 
ValueCountFrequency (%) 
01< 0.1%
 
11< 0.1%
 
21< 0.1%
 
31< 0.1%
 
41< 0.1%
 
ValueCountFrequency (%) 
99991< 0.1%
 
99981< 0.1%
 
99971< 0.1%
 
99961< 0.1%
 
99951< 0.1%
 

Lever position (lp) [ ]
Real number (ℝ≥0)

HIGH CORRELATION

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.1500753
Minimum1.138
Maximum9.3
Zeros0
Zeros (%)0.0%
Memory size78.1 KiB
2020-11-12T11:47:56.885221image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1.138
5-th percentile1.138
Q13.144
median5.14
Q37.148
95-th percentile9.3
Maximum9.3
Range8.162
Interquartile range (IQR)4.004

Descriptive statistics

Standard deviation2.624015432
Coefficient of variation (CV)0.50951011
Kurtosis-1.213124423
Mean5.1500753
Median Absolute Deviation (MAD)2.008
Skewness0.0275221423
Sum51500.753
Variance6.88545699
MonotocityNot monotonic
2020-11-12T11:47:57.370706image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%) 
4.161113311.3%
 
7.148112711.3%
 
1.138112611.3%
 
3.144111111.1%
 
8.206111011.1%
 
2.088110911.1%
 
5.14110811.1%
 
6.175108810.9%
 
9.3108810.9%
 
ValueCountFrequency (%) 
1.138112611.3%
 
2.088110911.1%
 
3.144111111.1%
 
4.161113311.3%
 
5.14110811.1%
 
ValueCountFrequency (%) 
9.3108810.9%
 
8.206111011.1%
 
7.148112711.3%
 
6.175108810.9%
 
5.14110811.1%
 

Ship speed (v) [knots]
Real number (ℝ≥0)

HIGH CORRELATION

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.9514
Minimum3
Maximum27
Zeros0
Zeros (%)0.0%
Memory size78.1 KiB
2020-11-12T11:47:57.542571image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3
Q19
median15
Q321
95-th percentile27
Maximum27
Range24
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.740467009
Coefficient of variation (CV)0.5177085095
Kurtosis-1.229499395
Mean14.9514
Median Absolute Deviation (MAD)6
Skewness0.005317656951
Sum149514
Variance59.91482952
MonotocityNot monotonic
2020-11-12T11:47:57.698790image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%) 
12113311.3%
 
21112711.3%
 
3112611.3%
 
9111111.1%
 
24111011.1%
 
6110911.1%
 
15110811.1%
 
18108810.9%
 
27108810.9%
 
ValueCountFrequency (%) 
3112611.3%
 
6110911.1%
 
9111111.1%
 
12113311.3%
 
15110811.1%
 
ValueCountFrequency (%) 
27108810.9%
 
24111011.1%
 
21112711.3%
 
18108810.9%
 
15110811.1%
 

Gas Turbine shaft torque (GTT) [kN m]
Real number (ℝ≥0)

HIGH CORRELATION

Distinct9634
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27100.64771
Minimum253.547
Maximum72783.338
Zeros0
Zeros (%)0.0%
Memory size78.1 KiB
2020-11-12T11:47:57.901902image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum253.547
5-th percentile2799.30905
Q18375.7915
median21630.1335
Q339000.71025
95-th percentile72770.53605
Maximum72783.338
Range72529.791
Interquartile range (IQR)30624.91875

Descriptive statistics

Standard deviation22062.78031
Coefficient of variation (CV)0.8141052767
Kurtosis-0.4907658011
Mean27100.64771
Median Absolute Deviation (MAD)16130.772
Skewness0.7726586677
Sum271006477.1
Variance486766274.9
MonotocityNot monotonic
2020-11-12T11:47:58.151902image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
50994.5063< 0.1%
 
50993.9743< 0.1%
 
8378.7053< 0.1%
 
14721.6693< 0.1%
 
50994.5953< 0.1%
 
50992.8633< 0.1%
 
14722.7933< 0.1%
 
14724.0993< 0.1%
 
29761.6953< 0.1%
 
14717.7183< 0.1%
 
Other values (9624)997099.7%
 
ValueCountFrequency (%) 
253.5471< 0.1%
 
267.6681< 0.1%
 
268.7381< 0.1%
 
269.3621< 0.1%
 
276.6991< 0.1%
 
ValueCountFrequency (%) 
72783.3381< 0.1%
 
72782.9991< 0.1%
 
72782.8021< 0.1%
 
72782.7581< 0.1%
 
72782.591< 0.1%
 

Gas Turbine rate of revolutions (GTn) [rpm]
Real number (ℝ≥0)

HIGH CORRELATION

Distinct3422
Distinct (%)34.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2131.151788
Minimum1307.675
Maximum3560.741
Zeros0
Zeros (%)0.0%
Memory size78.1 KiB
2020-11-12T11:47:58.418386image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1307.675
5-th percentile1346.1229
Q11386.758
median1924.325
Q32678.078
95-th percentile3560.405
Maximum3560.741
Range2253.066
Interquartile range (IQR)1291.32

Descriptive statistics

Standard deviation772.2117315
Coefficient of variation (CV)0.3623447827
Kurtosis-1.084236034
Mean2131.151788
Median Absolute Deviation (MAD)537.596
Skewness0.574645572
Sum21311517.88
Variance596310.9583
MonotocityNot monotonic
2020-11-12T11:47:58.652741image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
2678.078600.6%
 
2678.077540.5%
 
2678.076510.5%
 
1547.463450.4%
 
2678.075440.4%
 
1547.455430.4%
 
1547.459430.4%
 
1547.454420.4%
 
2678.082410.4%
 
1547.458410.4%
 
Other values (3412)953695.4%
 
ValueCountFrequency (%) 
1307.6751< 0.1%
 
1308.21< 0.1%
 
1308.2261< 0.1%
 
1308.3711< 0.1%
 
1308.7221< 0.1%
 
ValueCountFrequency (%) 
3560.7411< 0.1%
 
3560.741< 0.1%
 
3560.7261< 0.1%
 
3560.721< 0.1%
 
3560.7031< 0.1%
 

Gas Generator rate of revolutions (GGn) [rpm]
Real number (ℝ≥0)

HIGH CORRELATION

Distinct9926
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8194.182366
Minimum6589.002
Maximum9797.103
Zeros0
Zeros (%)0.0%
Memory size78.1 KiB
2020-11-12T11:47:58.887100image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum6589.002
5-th percentile6659.2274
Q17056.5205
median8480.527
Q39132.1325
95-th percentile9762.36415
Maximum9797.103
Range3208.101
Interquartile range (IQR)2075.612

Descriptive statistics

Standard deviation1090.569425
Coefficient of variation (CV)0.1330906949
Kurtosis-1.492093721
Mean8194.182366
Median Absolute Deviation (MAD)827.5685
Skewness-0.1319092547
Sum81941823.66
Variance1189341.672
MonotocityNot monotonic
2020-11-12T11:47:59.121457image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
6589.00260.1%
 
8780.013< 0.1%
 
8782.0243< 0.1%
 
8781.0173< 0.1%
 
8788.1012< 0.1%
 
9115.8282< 0.1%
 
9131.9222< 0.1%
 
9309.1112< 0.1%
 
9135.4342< 0.1%
 
9759.2342< 0.1%
 
Other values (9916)997399.7%
 
ValueCountFrequency (%) 
6589.00260.1%
 
6592.9911< 0.1%
 
6595.1621< 0.1%
 
6595.8941< 0.1%
 
6596.0081< 0.1%
 
ValueCountFrequency (%) 
9797.1031< 0.1%
 
9796.0521< 0.1%
 
9795.7781< 0.1%
 
9795.3861< 0.1%
 
9795.1051< 0.1%
 

Starboard Propeller Torque (Ts) [kN]
Real number (ℝ≥0)

HIGH CORRELATION

Distinct3880
Distinct (%)38.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean225.957785
Minimum5.304
Maximum645.249
Zeros0
Zeros (%)0.0%
Memory size78.1 KiB
2020-11-12T11:47:59.372547image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum5.304
5-th percentile8.17445
Q160.317
median175.264
Q3332.36
95-th percentile644.948
Maximum645.249
Range639.945
Interquartile range (IQR)272.043

Descriptive statistics

Standard deviation199.7378861
Coefficient of variation (CV)0.8839610731
Kurtosis-0.4103431475
Mean225.957785
Median Absolute Deviation (MAD)148.5595
Skewness0.8137916195
Sum2259577.85
Variance39895.22315
MonotocityNot monotonic
2020-11-12T11:47:59.625759image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
60.346250.2%
 
60.337230.2%
 
60.348220.2%
 
113.748220.2%
 
60.352220.2%
 
60.334210.2%
 
113.758200.2%
 
113.79200.2%
 
113.772200.2%
 
60.344190.2%
 
Other values (3870)978697.9%
 
ValueCountFrequency (%) 
5.3041< 0.1%
 
5.3131< 0.1%
 
5.3181< 0.1%
 
5.341< 0.1%
 
5.3411< 0.1%
 
ValueCountFrequency (%) 
645.2491< 0.1%
 
645.2361< 0.1%
 
645.2131< 0.1%
 
645.1822< 0.1%
 
645.1811< 0.1%
 

Port Propeller Torque (Tp) [kN]
Real number (ℝ≥0)

HIGH CORRELATION

Distinct3880
Distinct (%)38.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean225.957785
Minimum5.304
Maximum645.249
Zeros0
Zeros (%)0.0%
Memory size78.1 KiB
2020-11-12T11:47:59.864751image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum5.304
5-th percentile8.17445
Q160.317
median175.264
Q3332.36
95-th percentile644.948
Maximum645.249
Range639.945
Interquartile range (IQR)272.043

Descriptive statistics

Standard deviation199.7378861
Coefficient of variation (CV)0.8839610731
Kurtosis-0.4103431475
Mean225.957785
Median Absolute Deviation (MAD)148.5595
Skewness0.8137916195
Sum2259577.85
Variance39895.22315
MonotocityNot monotonic
2020-11-12T11:48:00.130355image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
60.346250.2%
 
60.337230.2%
 
60.348220.2%
 
113.748220.2%
 
60.352220.2%
 
60.334210.2%
 
113.758200.2%
 
113.79200.2%
 
113.772200.2%
 
60.344190.2%
 
Other values (3870)978697.9%
 
ValueCountFrequency (%) 
5.3041< 0.1%
 
5.3131< 0.1%
 
5.3181< 0.1%
 
5.341< 0.1%
 
5.3411< 0.1%
 
ValueCountFrequency (%) 
645.2491< 0.1%
 
645.2361< 0.1%
 
645.2131< 0.1%
 
645.1822< 0.1%
 
645.1811< 0.1%
 

HP Turbine exit temperature (T48) [C]
Real number (ℝ≥0)

HIGH CORRELATION

Distinct9890
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean734.4761775
Minimum444.731
Maximum1115.797
Zeros0
Zeros (%)0.0%
Memory size78.1 KiB
2020-11-12T11:48:00.378822image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum444.731
5-th percentile504.4347
Q1589.841
median705.351
Q3833.15475
95-th percentile1078.20325
Maximum1115.797
Range671.066
Interquartile range (IQR)243.31375

Descriptive statistics

Standard deviation173.0091567
Coefficient of variation (CV)0.2355544836
Kurtosis-0.6176778941
Mean734.4761775
Median Absolute Deviation (MAD)120.235
Skewness0.5693826172
Sum7344761.775
Variance29932.1683
MonotocityNot monotonic
2020-11-12T11:48:00.613181image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
574.6113< 0.1%
 
776.2292< 0.1%
 
586.3672< 0.1%
 
639.0692< 0.1%
 
698.8172< 0.1%
 
504.8712< 0.1%
 
588.1862< 0.1%
 
638.1862< 0.1%
 
834.162< 0.1%
 
828.5352< 0.1%
 
Other values (9880)997999.8%
 
ValueCountFrequency (%) 
444.7311< 0.1%
 
446.1761< 0.1%
 
446.6051< 0.1%
 
446.6831< 0.1%
 
446.9491< 0.1%
 
ValueCountFrequency (%) 
1115.7971< 0.1%
 
1114.8871< 0.1%
 
1114.6091< 0.1%
 
1113.9851< 0.1%
 
1113.7051< 0.1%
 
Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.1 KiB
288
10000 
ValueCountFrequency (%) 
28810000100.0%
 
2020-11-12T11:48:00.851359image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-11-12T11:48:01.007595image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:48:01.132588image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length5
Median length5
Mean length5
Min length5

GT Compressor outlet air temperature (T2) [C]
Real number (ℝ≥0)

HIGH CORRELATION

Distinct9694
Distinct (%)96.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean645.7489804
Minimum540.999
Maximum789.094
Zeros0
Zeros (%)0.0%
Memory size78.1 KiB
2020-11-12T11:48:01.321225image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum540.999
5-th percentile556.34095
Q1577.98075
median636.8
Q3693.65675
95-th percentile778.9491
Maximum789.094
Range248.095
Interquartile range (IQR)115.676

Descriptive statistics

Standard deviation72.48303638
Coefficient of variation (CV)0.1122464589
Kurtosis-1.024022885
Mean645.7489804
Median Absolute Deviation (MAD)58.1345
Skewness0.4335160588
Sum6457489.804
Variance5253.790563
MonotocityNot monotonic
2020-11-12T11:48:01.586828image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
559.3314< 0.1%
 
772.083< 0.1%
 
561.6523< 0.1%
 
559.9283< 0.1%
 
559.2383< 0.1%
 
577.2153< 0.1%
 
779.4533< 0.1%
 
732.9152< 0.1%
 
683.3142< 0.1%
 
605.7312< 0.1%
 
Other values (9684)997299.7%
 
ValueCountFrequency (%) 
540.9991< 0.1%
 
541.0311< 0.1%
 
541.7431< 0.1%
 
541.9731< 0.1%
 
542.2881< 0.1%
 
ValueCountFrequency (%) 
789.0941< 0.1%
 
788.9491< 0.1%
 
788.8061< 0.1%
 
788.7221< 0.1%
 
788.6641< 0.1%
 

HP Turbine exit pressure (P48) [bar]
Real number (ℝ≥0)

HIGH CORRELATION

Distinct523
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.3457322
Minimum1.093
Maximum4.56
Zeros0
Zeros (%)0.0%
Memory size78.1 KiB
2020-11-12T11:48:01.821170image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1.093
5-th percentile1.195
Q11.389
median2.082
Q32.981
95-th percentile4.516
Maximum4.56
Range3.467
Interquartile range (IQR)1.592

Descriptive statistics

Standard deviation1.081037747
Coefficient of variation (CV)0.460853011
Kurtosis-0.6925753588
Mean2.3457322
Median Absolute Deviation (MAD)0.796
Skewness0.7142598789
Sum23457.322
Variance1.168642611
MonotocityNot monotonic
2020-11-12T11:48:02.071156image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1.3891982.0%
 
1.391941.9%
 
1.3911921.9%
 
1.3921631.6%
 
1.3881401.4%
 
1.6621131.1%
 
1.6591111.1%
 
1.661101.1%
 
1.6611081.1%
 
1.6571001.0%
 
Other values (513)857185.7%
 
ValueCountFrequency (%) 
1.0933< 0.1%
 
1.0944< 0.1%
 
1.09550.1%
 
1.09660.1%
 
1.09770.1%
 
ValueCountFrequency (%) 
4.561< 0.1%
 
4.5582< 0.1%
 
4.5573< 0.1%
 
4.5564< 0.1%
 
4.5552< 0.1%
 
Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size78.1 KiB
0.998
10000 
ValueCountFrequency (%) 
0.99810000100.0%
 
2020-11-12T11:48:02.243037image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

GT Compressor outlet air pressure (P2) [bar]
Real number (ℝ≥0)

HIGH CORRELATION

Distinct4000
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.2625967
Minimum5.828
Maximum23.14
Zeros0
Zeros (%)0.0%
Memory size78.1 KiB
2020-11-12T11:48:02.371882image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum5.828
5-th percentile6.40795
Q17.445
median11.086
Q315.649
95-th percentile22.666
Maximum23.14
Range17.312
Interquartile range (IQR)8.204

Descriptive statistics

Standard deviation5.320817143
Coefficient of variation (CV)0.4339062332
Kurtosis-0.8288692163
Mean12.2625967
Median Absolute Deviation (MAD)4.1405
Skewness0.6357439058
Sum122625.967
Variance28.31109507
MonotocityNot monotonic
2020-11-12T11:48:02.621864image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
6.575100.1%
 
7.5390.1%
 
7.47990.1%
 
7.54590.1%
 
7.48780.1%
 
7.41680.1%
 
7.45280.1%
 
7.44380.1%
 
7.55980.1%
 
7.55280.1%
 
Other values (3990)991599.2%
 
ValueCountFrequency (%) 
5.8281< 0.1%
 
5.8292< 0.1%
 
5.831< 0.1%
 
5.8311< 0.1%
 
5.8323< 0.1%
 
ValueCountFrequency (%) 
23.141< 0.1%
 
23.1331< 0.1%
 
23.1261< 0.1%
 
23.121< 0.1%
 
23.1121< 0.1%
 

Gas Turbine exhaust gas pressure (Pexh) [bar]
Real number (ℝ≥0)

HIGH CORRELATION

Distinct19
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.029402
Minimum1.019
Maximum1.052
Zeros0
Zeros (%)0.0%
Memory size78.1 KiB
2020-11-12T11:48:02.840600image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1.019
5-th percentile1.019
Q11.02
median1.026
Q31.036
95-th percentile1.05
Maximum1.052
Range0.033
Interquartile range (IQR)0.016

Descriptive statistics

Standard deviation0.01035334319
Coefficient of variation (CV)0.01005762879
Kurtosis-0.6401060315
Mean1.029402
Median Absolute Deviation (MAD)0.006
Skewness0.7799309476
Sum10294.02
Variance0.0001071917152
MonotocityNot monotonic
2020-11-12T11:48:03.012463image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%) 
1.019161516.2%
 
1.02151315.1%
 
1.0268148.1%
 
1.037447.4%
 
1.0236366.4%
 
1.0365855.9%
 
1.0415525.5%
 
1.0425475.5%
 
1.0355425.4%
 
1.0224975.0%
 
Other values (9)195519.6%
 
ValueCountFrequency (%) 
1.019161516.2%
 
1.02151315.1%
 
1.0212182.2%
 
1.0224975.0%
 
1.0236366.4%
 
ValueCountFrequency (%) 
1.0521211.2%
 
1.0513583.6%
 
1.053553.5%
 
1.0492542.5%
 
1.04390.1%
 

Turbine Injecton Control (TIC) [%]
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct7403
Distinct (%)74.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.4807154
Minimum0
Maximum92.556
Zeros626
Zeros (%)6.3%
Memory size78.1 KiB
2020-11-12T11:48:03.246806image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q113.534
median25.2495
Q344.5
95-th percentile89.4492
Maximum92.556
Range92.556
Interquartile range (IQR)30.966

Descriptive statistics

Standard deviation25.7217254
Coefficient of variation (CV)0.7682549518
Kurtosis-0.07396039598
Mean33.4807154
Median Absolute Deviation (MAD)13.171
Skewness0.9053433904
Sum334807.154
Variance661.6071573
MonotocityNot monotonic
2020-11-12T11:48:03.497921image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
06266.3%
 
12.492100.1%
 
12.008100.1%
 
12.76290.1%
 
12.22490.1%
 
12.59390.1%
 
12.72890.1%
 
12.47590.1%
 
12.54290.1%
 
12.57680.1%
 
Other values (7393)929292.9%
 
ValueCountFrequency (%) 
06266.3%
 
0.0131< 0.1%
 
0.0191< 0.1%
 
0.0291< 0.1%
 
0.0331< 0.1%
 
ValueCountFrequency (%) 
92.5561< 0.1%
 
92.4761< 0.1%
 
92.4481< 0.1%
 
92.3971< 0.1%
 
92.3691< 0.1%
 

Fuel flow (mf) [kg/s]
Real number (ℝ≥0)

HIGH CORRELATION

Distinct694
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6591004
Minimum0.068
Maximum1.832
Zeros0
Zeros (%)0.0%
Memory size78.1 KiB
2020-11-12T11:48:03.732281image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.068
5-th percentile0.134
Q10.246
median0.496
Q30.881
95-th percentile1.771
Maximum1.832
Range1.764
Interquartile range (IQR)0.635

Descriptive statistics

Standard deviation0.5047905955
Coefficient of variation (CV)0.7658781507
Kurtosis-0.05625338919
Mean0.6591004
Median Absolute Deviation (MAD)0.264
Skewness1.00949829
Sum6591.004
Variance0.2548135453
MonotocityNot monotonic
2020-11-12T11:48:03.982263image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.242780.8%
 
0.241770.8%
 
0.244680.7%
 
0.243680.7%
 
0.24680.7%
 
0.245640.6%
 
0.238640.6%
 
0.246610.6%
 
0.239590.6%
 
0.247560.6%
 
Other values (684)933793.4%
 
ValueCountFrequency (%) 
0.0681< 0.1%
 
0.0692< 0.1%
 
0.0760.1%
 
0.0713< 0.1%
 
0.072100.1%
 
ValueCountFrequency (%) 
1.8321< 0.1%
 
1.8311< 0.1%
 
1.831< 0.1%
 
1.8291< 0.1%
 
1.8282< 0.1%
 
Distinct51
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9750208
Minimum0.95
Maximum1
Zeros0
Zeros (%)0.0%
Memory size78.1 KiB
2020-11-12T11:48:04.232245image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.95
5-th percentile0.952
Q10.962
median0.975
Q30.988
95-th percentile0.998
Maximum1
Range0.05
Interquartile range (IQR)0.026

Descriptive statistics

Standard deviation0.01473394979
Coefficient of variation (CV)0.01511142099
Kurtosis-1.201672776
Mean0.9750208
Median Absolute Deviation (MAD)0.013
Skewness0.005292485974
Sum9750.208
Variance0.0002170892763
MonotocityNot monotonic
2020-11-12T11:48:04.486974image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.9632082.1%
 
0.9982072.1%
 
0.9692062.1%
 
0.9512052.1%
 
0.982052.1%
 
0.9592052.1%
 
0.9822052.1%
 
0.9772042.0%
 
0.9532022.0%
 
0.9682022.0%
 
Other values (41)795179.5%
 
ValueCountFrequency (%) 
0.951841.8%
 
0.9512052.1%
 
0.9521871.9%
 
0.9532022.0%
 
0.9541941.9%
 
ValueCountFrequency (%) 
12022.0%
 
0.9992022.0%
 
0.9982072.1%
 
0.9971962.0%
 
0.9961921.9%
 
Distinct26
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.987455
Minimum0.975
Maximum1
Zeros0
Zeros (%)0.0%
Memory size78.1 KiB
2020-11-12T11:48:04.721319image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0.975
5-th percentile0.976
Q10.981
median0.987
Q30.994
95-th percentile0.999
Maximum1
Range0.025
Interquartile range (IQR)0.013

Descriptive statistics

Standard deviation0.007509541552
Coefficient of variation (CV)0.007604945594
Kurtosis-1.204308551
Mean0.987455
Median Absolute Deviation (MAD)0.006
Skewness0.008138611445
Sum9874.55
Variance5.639321432e-05
MonotocityNot monotonic
2020-11-12T11:48:04.971315image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%) 
0.9843954.0%
 
0.9813943.9%
 
0.9753933.9%
 
0.9773923.9%
 
0.9763913.9%
 
0.9993903.9%
 
0.9833893.9%
 
0.9963883.9%
 
0.9973873.9%
 
0.9913873.9%
 
Other values (16)609460.9%
 
ValueCountFrequency (%) 
0.9753933.9%
 
0.9763913.9%
 
0.9773923.9%
 
0.9783843.8%
 
0.9793763.8%
 
ValueCountFrequency (%) 
13773.8%
 
0.9993903.9%
 
0.9983853.9%
 
0.9973873.9%
 
0.9963883.9%
 

Interactions

2020-11-12T11:46:42.062485image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:46:42.871178image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:46:43.089909image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:46:43.325396image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:46:43.544128image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:46:43.778470image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:46:44.012846image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:46:44.231565image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:46:44.439563image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:46:44.673923image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:46:44.896631image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:46:45.108866image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:46:45.315483image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:46:45.581071image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:46:45.803614image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:46:46.023213image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:46:46.255347image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:46:46.521200image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:46:46.771184image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:46:47.052408image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:46:47.303559image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:46:47.564311image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:46:47.841893image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:46:48.155421image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:46:48.406528image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:46:48.763280image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:46:49.185126image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:46:49.471372image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:46:49.736983image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:46:50.026803image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:46:50.402463image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:46:50.685865image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:46:51.023057image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:46:51.397520image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:46:51.689285image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:46:51.892411image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:46:52.300756image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:46:52.550742image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:46:52.816345image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:46:53.081953image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:46:53.317456image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:46:53.564679image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:46:53.785213image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:46:54.003947image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:46:54.238304image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:46:54.517907image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:46:54.753828image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:46:55.019413image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:46:55.265682image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:46:55.532343image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:46:55.849982image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:46:56.068719image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:46:56.429349image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:46:56.741832image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:46:57.085553image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:46:57.399147image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:46:57.664771image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:46:57.977228image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:46:58.227213image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:46:58.512313image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:46:58.793543image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:46:59.246651image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:46:59.565081image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:46:59.879870image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:00.176725image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:00.472092image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:00.725678image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:00.960060image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:01.241269image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:01.476784image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:01.726768image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:01.961104image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:02.242336image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:02.449567image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:02.699555image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:02.933907image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:03.168269image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:03.419360image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:03.669341image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:03.934946image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:04.216175image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:04.486741image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:04.693170image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:04.943152image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:05.177513image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:05.409385image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:05.630257image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:05.864621image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:06.083356image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:06.340214image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:06.574575image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:06.808917image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:07.027651image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:07.277628image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:07.513134image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:07.747490image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:07.966227image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:08.200565image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:08.467290image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:08.686022image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:08.901855image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:09.136212image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:09.378413image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:09.599241image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:09.989840image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:10.209706image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:10.434069image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:10.655961image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:10.890337image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:11.109074image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:11.344554image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:11.563286image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:11.782026image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:12.000757image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:12.235114image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:12.470613image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:12.673722image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:12.893564image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:13.127925image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:13.363398image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:13.582152image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:13.800887image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:14.019603image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:14.253960image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:14.483968image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:14.702716image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:14.921433image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:15.140168image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:15.362386image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:15.581103image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:15.802063image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:16.036426image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:16.270800image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:16.490663image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:16.709418image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:16.928150image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:17.146871image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:17.382386image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:17.601133image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:17.819870image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:18.038589image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:18.272954image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:18.512182image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:18.730916image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:18.965278image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:19.184010image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:19.420551image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:19.641870image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:19.876211image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:20.110574image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:20.343117image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:20.608744image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:20.829580image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:21.048334image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:21.282673image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:21.518159image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:21.939991image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:22.158725image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:22.387317image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:22.606033image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:22.856016image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:23.074770image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:23.310251image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:23.513343image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:23.747721image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:23.966455image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:24.185169image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:24.424093image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:24.642828image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:24.861562image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:25.080311image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:25.300541image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:25.534899image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:25.772470image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:26.006829image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:26.241187image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:26.496039image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:26.730398image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:26.964753image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:27.199113image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:27.434618image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:27.668998image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:27.903337image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:28.137696image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:28.373174image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:28.607536image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:28.841894image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:29.060612image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:29.311725image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:29.622435image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:29.846307image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:30.084709image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:30.323238image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:30.558659image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:30.795115image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:31.013847image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:31.263843image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:31.514946image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:31.749293image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:31.983647image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:32.233627image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:32.516082image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:32.781696image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:33.016048image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:33.234788image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:33.470293image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:33.704669image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:33.923382image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:34.157764image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:34.421486image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:34.655846image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:34.894530image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:35.113251image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:35.410192image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:35.646679image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:35.881039image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:36.131020image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:36.397759image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:36.616512image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:36.850873image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:37.352052image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:37.617656image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:37.820767image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:38.039502image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:38.242631image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:38.465188image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:38.668279image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:38.887034image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:39.090129image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:39.309988image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:39.614416image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:39.911271image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:40.239373image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:40.585901image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:40.950366image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:41.231595image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:41.514503image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:41.733237image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:42.030108image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:42.248848image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:42.481057image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:42.705708image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:42.955669image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:43.190051image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:43.409903image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:43.644261image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:43.878622image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:44.112979image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:44.348451image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:44.585881image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:44.835884image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:45.070225image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:45.300516image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:45.534856image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:45.771334image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:46.005692image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:46.224447image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:46.450011image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:46.653144image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:46.887482image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:47.106219image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:47.357325image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:47.582071image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:47.810382image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:48.044720image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:48.263474image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:48.498945image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:48.733303image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:48.967664image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:49.186414image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:49.410031image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:49.628771image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:49.863129image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:50.081866image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:50.308425image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:50.511926image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:50.749485image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:50.968202image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:51.202581image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:51.422406image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:51.641140image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:51.875500image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:52.094235image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:52.314097image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:52.548457image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:52.782814image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:52.985926image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:53.220263image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:53.440127image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2020-11-12T11:48:05.205674image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-11-12T11:48:05.958218image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-11-12T11:48:06.621261image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-11-12T11:48:07.294174image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-11-12T11:47:54.080700image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2020-11-12T11:47:55.239203image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

Unnamed: 0Lever position (lp) [ ]Ship speed (v) [knots]Gas Turbine shaft torque (GTT) [kN m]Gas Turbine rate of revolutions (GTn) [rpm]Gas Generator rate of revolutions (GGn) [rpm]Starboard Propeller Torque (Ts) [kN]Port Propeller Torque (Tp) [kN]HP Turbine exit temperature (T48) [C]GT Compressor inlet air temperature (T1) [C]GT Compressor outlet air temperature (T2) [C]HP Turbine exit pressure (P48) [bar]GT Compressor inlet air pressure (P1) [bar]GT Compressor outlet air pressure (P2) [bar]Gas Turbine exhaust gas pressure (Pexh) [bar]Turbine Injecton Control (TIC) [%]Fuel flow (mf) [kg/s]GT Compressor decay state coefficient.GT Turbine decay state coefficient.
009.30027.072762.2053560.3939753.812644.806644.8061086.583288.0780.3044.5230.99822.8791.05090.4351.7900.9730.978
116.17518.029760.5522306.8258780.012246.011246.011776.921288.0665.5112.5180.99813.4381.03034.5960.6850.9950.975
223.1449.08375.7741386.7397051.62160.31860.318589.764288.0580.5871.3920.9987.5661.02012.4770.2470.9800.978
333.1449.08377.5891386.7487098.46960.33960.339570.651288.0576.5651.3900.9987.4091.02111.6780.2310.9841.000
446.17518.029761.0912306.8258782.024246.021246.021769.855288.0663.6822.5180.99813.3741.03134.1540.6760.9980.980
557.14821.038990.3242678.0689126.509332.301332.301837.934288.0693.7062.9720.99815.6491.03544.8620.8880.9670.980
668.20624.050994.8543087.5339311.074438.083438.083930.553288.0734.4853.5820.99818.5021.04160.8551.2050.9680.984
776.17518.029798.1562307.2178847.578246.318246.318769.867288.0669.4162.4950.99813.0281.03033.8140.6690.9561.000
887.14821.038999.2582678.0589121.944332.401332.401816.202288.0686.3682.9790.99815.5581.03643.4610.8610.9860.989
999.30027.072779.4423560.4369789.399644.966644.9661090.551288.0784.2014.4770.99822.2501.04989.8601.7790.9550.997

Last rows

Unnamed: 0Lever position (lp) [ ]Ship speed (v) [knots]Gas Turbine shaft torque (GTT) [kN m]Gas Turbine rate of revolutions (GTn) [rpm]Gas Generator rate of revolutions (GGn) [rpm]Starboard Propeller Torque (Ts) [kN]Port Propeller Torque (Tp) [kN]HP Turbine exit temperature (T48) [C]GT Compressor inlet air temperature (T1) [C]GT Compressor outlet air temperature (T2) [C]HP Turbine exit pressure (P48) [bar]GT Compressor inlet air pressure (P1) [bar]GT Compressor outlet air pressure (P2) [bar]Gas Turbine exhaust gas pressure (Pexh) [bar]Turbine Injecton Control (TIC) [%]Fuel flow (mf) [kg/s]GT Compressor decay state coefficient.GT Turbine decay state coefficient.
999099905.14015.021632.7571924.3158484.844175.274175.274719.972288.0641.3782.0820.99811.1721.02625.8080.5110.9640.979
999199911.1383.04043.5721369.4606605.1208.5968.596559.875288.0559.3311.2390.9986.7261.0199.1430.2050.9990.980
999299927.14821.038993.2982678.0629124.038332.248332.248837.996288.0693.1212.9750.99815.6931.03544.9190.8890.9700.978
999399935.14015.021637.6331924.3468506.131175.341175.341706.761288.0639.8182.0750.99810.9711.02625.0090.4950.9580.996
999499942.0886.06643.9261384.7156827.38329.74729.747588.510288.0570.4561.3250.9987.0761.0204.0170.2430.9811.000
999599952.0886.05858.7801349.0236736.27323.17123.171581.017288.0564.9221.2940.9986.9911.01921.5830.2450.9990.988
999699965.14015.021633.7431924.3498497.158175.288175.288696.232288.0635.8942.0780.99810.9471.02624.4810.4850.9711.000
999799978.20624.050994.8193087.5559324.455438.051438.051928.531288.0737.4433.5600.99818.2001.04160.3061.1940.9530.996
999899986.17518.029761.0782306.8508793.302245.973245.973783.490288.0668.5582.5130.99813.4121.03034.9190.6910.9820.975
999999998.20624.050990.5043087.3299300.796438.031438.031929.296288.0731.6103.5970.99818.6941.04161.0341.2080.9810.977